Uniform recovery of fusion frame structured sparse signals

TL;DR

This paper demonstrates that fusion frame sparse signals can be uniformly recovered from fewer measurements using mixed l^1/l^2-minimization, with stability guarantees and optimal measurement bounds established.

Contribution

It introduces a method for uniform recovery of fusion frame sparse signals with reduced measurements, leveraging coherence parameters and establishing optimal measurement bounds.

Findings

Uniform recovery achieved with fewer vector-valued measurements.

Scalar-valued subgaussian measurements are optimal for uniform recovery.

Provides stability guarantees for noisy and approximately sparse signals.

Abstract

We consider the problem of recovering fusion frame sparse signals from incomplete measurements. These signals are composed of a small number of nonzero blocks taken from a family of subspaces. First, we show that, by using a-priori knowledge of a coherence parameter associated with the angles between the subspaces, one can uniformly recover fusion frame sparse signals with a significantly reduced number of vector-valued (sub-)Gaussian measurements via mixed l^1/l^2-minimization. We prove this by establishing an appropriate version of the restricted isometry property. Our result complements previous nonuniform recovery results in this context, and provides stronger stability guarantees for noisy measurements and approximately sparse signals. Second, we determine the minimal number of scalar-valued measurements needed to uniformly recover all fusion frame sparse signals via mixed l^1/l^2-minimization. This bound is achieved by scalar-valued subgaussian measurements. In particular, our result shows that the number of scalar-valued subgaussian measurements cannot be further reduced using knowledge of the coherence parameter. As a special case it implies that the best known uniform recovery result for block sparse signals using subgaussian measurements is optimal.